library(sf)
## Linking to GEOS 3.5.1, GDAL 2.1.3, proj.4 4.9.2, lwgeom 2.3.2 r15302
library(raster)
## Loading required package: sp
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:raster':
##
## intersect, select, union
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(mapview)
## Loading required package: leaflet
library(RColorBrewer)
data('cookfarm', package = "GSIF")
names(cookfarm)
## [1] "readings" "profiles" "bdensity" "grids" "weather"
## [6] "proj4string"
grids <- cookfarm$grids %>%
dplyr::select(x, y, DEM, TWI, Cook_fall_ECa, Cook_spr_ECa) %>%
rasterFromXYZ(crs = cookfarm$proj4string)
plot(grids)

profiles <- cookfarm$profiles %>%
st_as_sf(coords = c('Easting', 'Northing'), crs = cookfarm$proj4string)
plot(profiles)

# Plot points
mapview(profiles)
mapview(profiles, zcol = "BLD")
mapview(profiles, zcol = "TAXSUSDA")
pal_continuous <- colorRampPalette(brewer.pal(7, "BrBG"))
pal_categorical <- colorRampPalette(brewer.pal(9, "Set1"))
mapview(profiles, zcol = "BLD", col.regions = pal_continuous, legend = TRUE)
mapview(profiles, zcol = "TAXSUSDA", col.regions = pal_categorical, legend = TRUE)
# Plot grids
mapview(grids)
mapview(grids, col.regions = pal_continuous, legend = TRUE)
mapview(grids$TWI, col.regions = pal_continuous, legend = TRUE)
# Plot both together
mapview(grids$DEM, col.regions = pal_continuous, legend = TRUE) + mapview(profiles, zcol = "TAXSUSDA", col.regions = pal_categorical)
# Burst to separate soil classes
mapview(profiles, zcol = "TAXSUSDA", col.regions = pal_categorical, legend = TRUE, burst = TRUE)
# Syncing several maps
m1 <- mapview(grids$DEM)
m2 <- mapview(grids$TWI)
m3 <- mapview(profiles)
sync(m1, m2, m3)
# Raster specific functions
viewRGB(poppendorf)
viewRGB(poppendorf, 4, 3, 2)
# plainview
plainview(poppendorf[[1]])
plainview(poppendorf[[1]], at = seq(8000, 11000, 50))
plainview(poppendorf, 4, 3, 2)
plainview(poppendorf, 5, 4, 3, quantiles = c(0.5, 1))
# slideview
slideview(poppendorf[[1]], poppendorf[[5]])
slideview(poppendorf[[1]], poppendorf[[5]], legend = FALSE)
img1 <- poppendorf[[1]]
img2 <- poppendorf[[5]]
slideview(img1, img2,
label1 = "Poppendorf-Layer-1",
label2 = "Poppendorf-Layer-2")
# cubeview
# stck <- raster::stack("tappelhans/uni/talks/jena_201703/data/barazar.tif")
# raster::nlayers(stck) * raster::ncell(stck) # takes a while!!
# cubeview(stck)
mapview(
profiles,
popup = popupImage('https://www.vcard.wur.nl/WebServices/GetMedia.ashx?id=37263')
)
Advanced mapview
library(xts)
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
##
## Attaching package: 'xts'
## The following objects are masked from 'package:dplyr':
##
## first, last
library(dygraphs)
profiles$SOURCEID <- as.character(profiles$SOURCEID)
records <- cookfarm$readings
records$SOURCEID <- as.character(records$SOURCEID)
ids <- unique(records$SOURCEID)
# Subset sensors
ids <- sample(ids, size = 5)
idx_sensors <- which(profiles$SOURCEID %in% ids)
sensors <- profiles[idx_sensors,]
make_ts <- function(id) {
records %>%
filter(SOURCEID == id) %>%
dplyr::select(-SOURCEID) %>%
dplyr::select(Date, ends_with('VW')) %>%
xts(.$Date)
}
make_dygraph <- function(id){
ts <- make_ts(id)
dygraph(ts)
}
l_graphs <- lapply(
ids,
make_dygraph
)
make_dygraph(ids[1])
mapview(sensors, popup = popupGraph(graphs = l_graphs, width = 300, height = 300))